Accurate presence detection data is difficult to consistently collect from furniture items experiencing interruptions from one or more sources. For example, a capacitive sensing mechanism associated with a furniture item in a first environment may encounter environment changes that alter and/or mask a measured change in capacitance. The same altered capacitance detection may further translate into a measured change in capacitance that surpasses a presence-indicating threshold, falsely triggering one or more features of the furniture item due to non-triggering events in the surrounding environment. Similarly, a capacitive sensing mechanism associated with a furniture item in a second environment may experience similar “noise” interruptions in capacitance detection that are different than those experienced by the capacitive sensing mechanism of the first environment, with the impact of such “noise” generating altered capacitance detection data that initiates additional triggering events. Accordingly, a need exists for an accurate presence-sensing technology for use with automated furniture, which addresses the foregoing and other problems.